Groundwater is one of the most important freshwater resources for mankind and for ecosystems. Assessing groundwater resources and developing sustainable water management plans based on this resource is a major field of activity for science, water authorities and consultancies worldwide. Due to its fundamental role in the Earth’s water and energy cycles, groundwater has been declared as an Essential Climate Variable (ECV) by GCOS, the Global Climate Observing System. The Copernicus Services, however, do not yet deliver data on this fundamental resource, nor is there any other data source worldwide that operationally provides information on changing groundwater resources in a consistent way, observation-based, and with global coverage. This gap will be closed by G3P, the Global Gravity-based Groundwater Product. The G3P consortium combines key expertise from science and industry across Europe that optimally allows to (1) capitalize from the unique capability of GRACE and GRACE-FO satellite gravimetry as the only remote sensing technology to monitor subsurface mass variations and thus groundwater storage change for large areas, (2) incorporate and advance a wealth of products on storage compartments of the water cycle that are part of the Copernicus portfolio, and (3) disseminate unprecedented information on changing groundwater storage to the global and European user communities, including a European use case as a demonstrator for industry potential in the water sector. In combination, the G3P development is a novel and cross-cutting extension of the Copernicus portfolio towards essential information on the changing state of water resources at European and global scales. G3P is timely given the recent launch of GRACE-FO that opens up the chance for gravity-based time series with sufficient length to monitor climate-induced and human-induced processes over more than 20 years, and to boost European space technology on board these satellites.

The EOSC Data Commons project’s mission is to contribute to tThe EOSC Data Commons project’s mission is to contribute to the establishment of EOSC as the European Research Commons, a global trusted ecosystem that provides seamless access to high-quality interoperable research outputs and services that enable European researchers to collaborate more easily, be more productive and achieve higher levels of excellence. The project achieves this with innovative EOSC Exchange services for improving and accelerating data lifecycle management supporting discovery, analysis, deposition, preservation, sharing, use and reuse of research data in a European data and compute continuum that builds on the capabilities of the EOSC EU Node, national and European infrastructures for data-intensive research and a community of federated repositories from national, institutional and thematic initiatives. The project will deliver: (1) a AI-based Analytics-Oriented Metadata Warehouse and Discovery Service; (2) a federation of data repositories from different providers enriched by scientific applications and data analytics tools; (3) a Catalogue of data analytics tools (4) an Execution Service for tool deployment and execution; (5) metadata specifications for the reproducible and interoperable execution of analytic tools; (6) a FAIRness assessment and reproducibility toolset and related policies. Innovation is led by multidisciplinary and thematic use cases from Social Sciences and Humanities, Physics, Life Sciences, Biology, Heath and Medicine, and Environmental Science. The project’s pan-European consortium involves open source technology providers, major national and thematic data repositories, and user communities contributing to co-design, testing and validation with their use cases. By working with 12 national and institutional data repositories, the project has the ambition of delivering new solutions that will support the integration of Nodes in the future EOSC Federation.

The EU Copernicus programme has established itself globally as the predominant spatial data provider, through the provision of massive streams of high resolution earth observation (EO) data. These data are used in environmental monitoring and climate change applications supporting European policy initiatives, such as the Green Deal and others. To date, there is no single European processing back-end that serves all datasets of interest, and Europe is falling behind international developments in big data analytics and computing. This situation limits the integration of these data in science and monitoring applications, particularly when expanding the applications to regional, continental, and global scales. The proposed C-SCALE (Copernicus - eoSC AnaLytics Engine) project aims to federate European EO infrastructure services, such as the Copernicus DIAS and others. The federation shall capitalise on the European Open Science Cloud’s (EOSC) capacity and capabilities to support Copernicus research and operations with large and easily accessible European computing environments. That would allow the rapid scaling and sharing of EO data among a large community of users by increasing the service offering of the EOSC Portal. By making such a scalable Big Copernicus Data Analytics federated services available through EOSC and its Portal and linking the problems and results with experience from other research disciplines, C-SCALE will help to support the EO sector in its development and furthermore will enable the integration of EO data into other existing and future domains within EOSC. By abstracting the set up of computing and storage resources from the end-users, C-SCALE will enable the deploying of custom workflows to quickly and easily generate meaningful results. The project will deliver a blueprint, setting up an interaction model between service providers to facilitate interoperability between commercial (e.g. DIAS-es) and public cloud infrastructures.

interTwin co-designs and implements the prototype of an interdisciplinary Digital Twin Engine (DTE), an open source platform that provides generic and tailored software components for modelling and simulation to integrate application-specific Digital Twins (DTs). Its specifications and implementation are based on a co-designed conceptual model - the DTE blueprint architecture - guided by the principles of open standards and interoperability. The ambition is to develop a common approach to the implementation of DTs that is applicable across the whole spectrum of scientific disciplines and beyond to facilitate developments and collaboration. Co-design involves DT use cases for High energy physics, Radio astronomy, Astroparticle physics, Climate research, and Environmental monitoring, whose complex requirements are expected to significantly advance the state of the art of modelling and simulation using heterogeneous distributed digital infrastructures, advanced workflow composition, real-time data management and processing, quality and uncertainty tracing of models, data fusion and analytics. As a result, a consolidation of software technologies supporting research will emerge. The validation of the technology with multiple infrastructure facilities, will boost the accessibility of users to technological capacity and the support of AI uptake in research. interTwin builds on the capacities of experts from pan-European research infrastructures and the long tail of science, an open source community of technology providers that will deliver TRL 6/7 capabilities to implement the interdisciplinary DTE, experts of the European Centre of Excellence in Exascale Computing, and infrastructure providers from the EGI Federation, PRACE and EuroHPC supporting data and compute intensive science. interTwin key exploitable results will be continually co-developed and aligned with the contribution of external initiatives such as Destination Earth, EOSC, EuroGEO and EU data spaces.

The ability to integrate data from multiple sources is nowadays a major competitive advantage for organizations. Data-driven applications using AI techniques are reshaping various industries such as manufacturing, tourism, and mobility. The European Strategy for Data aims to create a single market for data while ensuring Europe's global competitiveness and data sovereignty. This has led to the development of Common European data spaces, yet the governance of the data life cycle in organizations has not kept up with the rapid technology evolution and remains largely manual. This is especially evident in scenarios where tens or hundreds of continuously evolving data sources produce semi-structured data, and create significant challenges when governed manually, causing organizations to end up with data silos. A systematic and standardized mechanism is needed to ingest, integrate, and process data, thus boosting the ability to develop new data-centric business models. However, current research and development efforts typically target one aspect of the end-to-end data lifecycle, such as scalable data management, ML performance, AI explainability, or sharing, while dismissing its governance. To overcome this limitation, CyclOps proposes a new framework for the governance and maintenance of the complete data lifecycle for large-scale volumes of data generated in heterogeneous distributed sources to enable data sharing and exchange. CyclOps intelligently automates, by means of knowledge graphs (KGs) and with a human-in-the-loop approach, the generation and execution of data processing pipelines. KGs are the established formal models to represent data and metadata while providing context and guaranteeing interoperability with other systems adhering to the FAIR Guiding Principles. CyclOps will enable organizations to seamlessly provide, cross and analyze machine- and human-generated data from and for data spaces, thus facilitating the provision of added-value services on top.